CN113037786A - Intelligent computing power scheduling method, device and system - Google Patents
Intelligent computing power scheduling method, device and system Download PDFInfo
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- CN113037786A CN113037786A CN201911246796.8A CN201911246796A CN113037786A CN 113037786 A CN113037786 A CN 113037786A CN 201911246796 A CN201911246796 A CN 201911246796A CN 113037786 A CN113037786 A CN 113037786A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/50—Network services
- H04L67/60—Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources
- H04L67/63—Routing a service request depending on the request content or context
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Abstract
The disclosure provides an intelligent computing power scheduling method, device and system. After receiving user service demand information, the intelligent computing power scheduling device analyzes the user service demand into computing power demand and performs service grade division according to the computing power demand; acquiring cloud resources required by service function deployment from an enabling platform according to the service type and the divided service levels; according to the service level, selecting matched deployment nodes from the acquired cloud resources, and deploying the user virtual machine and the virtual service gateway on the deployment nodes; and configuring virtual service gateway routing so as to route user service traffic to a specified node to realize service online. According to the cloud resource scheduling method and device, cloud resource scheduling across resource pools can be achieved, and resource utilization rate and user experience are effectively improved.
Description
Technical Field
The present disclosure relates to the field of network communications, and in particular, to an intelligent computing power scheduling method, apparatus, and system.
Background
In the MEC (Mobile Edge Computing) field, Computing power resources of Edge nodes are very limited, while AI (Artificial Intelligence) services, video services and the like accessing to an Edge cloud have high burstiness, a fixed service Computing power distribution method is difficult to meet performance requirements during a service peak period, and waste of Edge Computing power resources is caused when the service is idle. The existing solution aims at solving the management problem of edge equipment and the communication problem of edge cloud, lacks of calculation force scheduling from a service angle, is difficult to ensure the service grade requirement of a service, and the service processing node sinks and needs to be matched with the downward movement of a service gateway, and the service processing node is simply deployed at the edge, so that the service flow cannot be ensured to directly flow to the edge node.
Disclosure of Invention
The invention provides a scheme capable of realizing calculation power distribution across resource pools, thereby effectively improving the resource utilization rate and user experience.
According to a first aspect of the embodiments of the present disclosure, there is provided an intelligent computing power scheduling method, including: after receiving user service demand information, analyzing the user service demand into computing power demand, and dividing service grades according to the computing power demand; acquiring cloud resources required by service function deployment from an enabling platform according to the service type and the divided service levels; according to the service level, selecting matched deployment nodes from the acquired cloud resources, and deploying user virtual machines and virtual service gateways on the deployment nodes; and configuring virtual service gateway routing so as to route user service traffic to a specified node to realize service online.
In some embodiments, after the business is online, the method further comprises: monitoring the change situation of the service demand along with time to predict the change trend of the service demand; and if the service level of the predicted service requirement changes, executing a step of acquiring cloud resources required by the service deployment function from the enabling platform according to the service type and the predicted service level.
In some embodiments, deploying the user virtual machine and the virtual service gateway on the deployment node comprises: deploying a user virtual machine and a virtual service gateway on the deployment node by utilizing a Network Function Virtualization Orchestrator (NFVO); predicting the change trend of the business demand comprises the following steps: and predicting the change trend of the service demand by using a long-time memory model (LSTM).
In some embodiments, the computational demand includes at least one of a network demand or a computational demand, wherein the network demand includes latency, bandwidth, packet loss rate, and the computational demand includes CPU core count, memory, accelerated resource type, and accelerated resource quantity.
In some embodiments, the cloud resources include core cloud computing power resources and edge cloud computing power resources.
According to a second aspect of the embodiments of the present disclosure, there is provided an intelligent computing power scheduling apparatus, including: the analysis module is configured to analyze the user service requirements into computing power requirements after receiving the user service requirement information, and perform service grade division according to the computing power requirements; the cloud resource acquisition module is configured to acquire cloud resources required by service function deployment from the enabling platform according to the service types and the divided service levels; the function deployment module is configured to select a matched deployment node from the acquired cloud resources according to the service level, and deploy a user virtual machine and a virtual service gateway on the deployment node; and the network configuration module is configured to configure the virtual service gateway route so as to route the user service flow to the specified node to realize the on-line of the service.
In some embodiments, the above apparatus further comprises: the monitoring module is configured to monitor the change situation of the service demand along with time after the service is online so as to predict the change trend of the service demand; the cloud resource obtaining module is further configured to execute a step of obtaining cloud resources required for deploying the business function from the enabling platform according to the business type and the predicted service level if the service level of the predicted business requirement changes.
In some embodiments, the function deployment module is configured to deploy the user virtual machines and the virtual service gateways on the deployment node using a network function virtualization orchestrator NFVO; the monitoring module is configured to predict a trend of the change of the business demand by using the long-time memory model LSTM.
In some embodiments, the computational demand includes at least one of a network demand or a computational demand, wherein the network demand includes latency, bandwidth, packet loss rate, and the computational demand includes CPU core count, memory, accelerated resource type, and accelerated resource quantity.
In some embodiments, the cloud resources include core cloud computing power resources and edge cloud computing power resources.
According to a third aspect of the embodiments of the present disclosure, there is provided an intelligent computing power scheduling apparatus, including: a memory configured to store instructions; a processor coupled to the memory, the processor configured to perform a method implementing any of the embodiments described above based on instructions stored by the memory.
According to a fourth aspect of the embodiments of the present disclosure, an intelligent computing power scheduling system is provided, which includes the intelligent computing power scheduling apparatus according to any one of the embodiments described above, and an enabling platform configured to feed back cloud resources required for deploying business functions to the intelligent computing power scheduling apparatus according to the business types and service levels provided by the intelligent computing power scheduling apparatus.
According to a fifth aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, in which computer instructions are stored, and when executed by a processor, the computer-readable storage medium implements the method according to any of the embodiments described above.
Other features of the present disclosure and advantages thereof will become apparent from the following detailed description of exemplary embodiments thereof, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description, serve to explain the principles of the disclosure.
The present disclosure may be more clearly understood from the following detailed description, taken with reference to the accompanying drawings, in which:
FIG. 1 is a schematic flow diagram of an intelligent computational power scheduling method according to one embodiment of the present disclosure;
FIG. 2 is a schematic flow diagram of an intelligent computational power scheduling method according to another embodiment of the present disclosure;
FIG. 3 is a schematic structural diagram of an intelligent computational power scheduling apparatus according to one embodiment of the present disclosure;
FIG. 4 is a schematic structural diagram of an intelligent computing power scheduling apparatus according to another embodiment of the present disclosure;
FIG. 5 is a schematic structural diagram of an intelligent computing power scheduling apparatus according to yet another embodiment of the present disclosure;
FIG. 6 is a schematic diagram of an intelligent computational power scheduling system, according to one embodiment of the present disclosure.
It should be understood that the dimensions of the various parts shown in the figures are not drawn to scale. Further, the same or similar reference numerals denote the same or similar components.
Detailed Description
Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. The description of the exemplary embodiments is merely illustrative and is in no way intended to limit the disclosure, its application, or uses. The present disclosure may be embodied in many different forms and is not limited to the embodiments described herein. These embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art. It should be noted that: the relative arrangement of parts and steps, the composition of materials and values set forth in these embodiments are to be construed as illustrative only and not as limiting unless otherwise specifically stated.
The use of the word "comprising" or "comprises" and the like in this disclosure means that the elements listed before the word encompass the elements listed after the word and do not exclude the possibility that other elements may also be encompassed.
All terms (including technical or scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs unless specifically defined otherwise. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.
Fig. 1 is a flow diagram of an intelligent computational power scheduling method according to one embodiment of the present disclosure. In some embodiments, the following intelligent computing power scheduling method steps are performed by an intelligent computing power scheduling apparatus.
In step 101, after receiving the user service requirement information, analyzing the user service requirement into a computing power requirement, and performing service level division according to the computing power requirement.
In some embodiments, the computational requirements include at least one of network requirements or computational requirements, wherein the network requirements include latency, bandwidth, packet loss rate, and the computational requirements include CPU core count, memory, accelerated resource type, and accelerated resource quantity.
In step 102, cloud resources required for deploying the service function are acquired from the enabling platform according to the service type and the divided service levels.
In some embodiments, the cloud resources include core cloud computing power resources and edge cloud computing power resources, including virtual machine deployment number, mirror image, type template (browser), and the like.
In step 103, according to the service level, a matched deployment node is selected from the acquired cloud resources, and a user virtual machine and a virtual service gateway are deployed on the deployment node.
In some embodiments, NFVO (Network Function Virtualization editor) is utilized to deploy user virtual machines and virtual service gateways on deployment nodes.
At step 104, a virtual service gateway route is configured to route user service traffic to a designated node to enable service on-line.
In the intelligent computing power scheduling method provided by the embodiment of the disclosure, the cloud resources required for deploying the service function are acquired from the enabling platform according to the service type and the divided service levels, so that the cloud resource call across the resource pool is realized, and the resource utilization rate is effectively improved.
Fig. 2 is a flow chart diagram of an intelligent computing power scheduling method according to another embodiment of the present disclosure. In some embodiments, the following intelligent computing power scheduling method steps are performed by an intelligent computing power scheduling apparatus.
In step 201, after receiving the user service requirement information, the user service requirement is analyzed into a calculation force requirement, and service level division is performed according to the calculation force requirement.
In some embodiments, the computational requirements include at least one of network requirements or computational requirements, wherein the network requirements include latency, bandwidth, packet loss rate, and the computational requirements include CPU core count, memory, accelerated resource type, and accelerated resource quantity.
In step 202, cloud resources required for deploying business functions are acquired from the enabling platform according to the business types and the divided service levels.
In some embodiments, the cloud resources include core cloud computing power resources and edge cloud computing power resources.
In step 203, according to the service level, a matched deployment node is selected from the acquired cloud resources, and a user virtual machine and a virtual service gateway are deployed on the deployment node.
In some embodiments, the NFVO is utilized to deploy user virtual machines and virtual service gateways on a deployment node.
In step 204, the virtual service gateway route is configured so as to route the user service traffic to the designated node to implement service on-line.
In step 205, the variation of the service demand over time is monitored to predict the variation trend of the service demand.
In some embodiments, the LSTM (Long-Short Term Memory model) is used to predict the trend of the change of the business demand.
And if the service level of the predicted service requirement changes, returning to the step 202, and executing the step of acquiring the cloud resources required by the service deployment function from the enabling platform according to the service type and the predicted service level.
The calculation power adjustment is realized by predicting the calculation power demand change, and the service level of the user can be effectively ensured.
Fig. 3 is a schematic structural diagram of an intelligent computing power scheduling apparatus according to an embodiment of the present disclosure. As shown in fig. 3, the intelligent computing power scheduling apparatus includes an analysis module 31, a cloud resource acquisition module 32, a function deployment module 33, and a network configuration module 34.
The parsing module 31 is configured to parse the user service requirement into the computing power requirement after receiving the user service requirement information, and perform service level division according to the computing power requirement.
In some embodiments, the computational requirements include at least one of network requirements or computational requirements, wherein the network requirements include latency, bandwidth, packet loss rate, and the computational requirements include CPU core count, memory, accelerated resource type, and accelerated resource quantity.
The cloud resource acquiring module 32 is configured to acquire cloud resources required for deploying the service function from the enabling platform according to the service type and the divided service level.
In some embodiments, the cloud resources include core cloud computing power resources and edge cloud computing power resources, including virtual machine deployment number, mirror image, type template (browser), and the like.
The function deployment module 33 is configured to select a matched deployment node from the acquired cloud resources according to the service level, and deploy the user virtual machine and the virtual service gateway on the deployment node.
In some embodiments, the function deployment module 33 deploys the user virtual machine and the virtual service gateway on the deployment node using the NFVO.
The network configuration module 34 is configured to configure virtual service gateway routing to route user traffic to a designated node to bring the traffic on-line.
In the intelligent computing power scheduling device provided by the embodiment of the disclosure, the cloud resources required for deploying the service function are acquired from the enabling platform according to the service type and the divided service levels, so that the cloud resource call across the resource pool is realized, and the resource utilization rate is effectively improved.
Fig. 4 is a schematic structural diagram of an intelligent computing power scheduling apparatus according to another embodiment of the present disclosure. Fig. 4 differs from fig. 3 in that, in the embodiment shown in fig. 4, the intelligent computing power scheduling apparatus further includes a monitoring module 35.
The monitoring module 35 is configured to monitor the change of the service demand with time after the service is online, so as to predict the change trend of the service demand.
In some embodiments, monitoring module 35 is configured to predict a trend of traffic demand using LSTM.
The cloud resource obtaining module 32 is further configured to, if the service level of the predicted service demand changes, perform an operation of obtaining, from the enabling platform, the cloud resources required for deploying the service function according to the service type and the predicted service level.
Fig. 5 is a schematic structural diagram of an intelligent computing power scheduling apparatus according to yet another embodiment of the present disclosure. As shown in fig. 5, the intelligent computing power scheduling apparatus includes a memory 51 and a processor 52.
The memory 51 is used to store instructions. The processor 52 is coupled to the memory 51. The processor 52 is configured to perform a method as referred to in any of the embodiments of fig. 1 and 2 based on the instructions stored by the memory.
As shown in fig. 5, the intelligent computing power scheduling apparatus further includes a communication interface 53 for information interaction with other devices. Meanwhile, the intelligent computing power scheduling device further comprises a bus 54, and the processor 52, the communication interface 53 and the memory 51 are communicated with each other through the bus 54.
The Memory 51 may include a Random Access Memory (RAM) or a Non-Volatile Memory (NVM). Such as at least one disk storage. The memory 51 may also be a memory array. The storage 51 may also be partitioned and the blocks may be combined into virtual volumes according to certain rules.
Further, the processor 52 may be a central processing unit, or may be an ASIC (Application Specific Integrated Circuit), or one or more Integrated circuits configured to implement embodiments of the present disclosure.
The present disclosure also provides a computer-readable storage medium. The computer-readable storage medium stores computer instructions, which when executed by the processor implement a method according to any one of the embodiments of fig. 1 or fig. 2.
FIG. 6 is a schematic diagram of an intelligent computational power scheduling system, according to one embodiment of the present disclosure. As shown in fig. 6, the intelligent computing power scheduling system includes an intelligent computing power scheduling device 61 and an enabling platform 62. The intelligent computing power scheduling device 61 is the intelligent computing power scheduling device according to any one of the embodiments shown in fig. 3 to 5.
The enabling platform 62 is configured to select the best matching function according to the service type and the service level provided by the intelligent computing power scheduling device, and feed back the cloud resources required for deploying the service function to the intelligent computing power scheduling device.
In some embodiments, the functional modules may be implemented as a general purpose Processor, a Programmable Logic Controller (PLC), a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), or other Programmable Logic device, discrete Gate or transistor Logic, discrete hardware components, or any suitable combination thereof, for performing the functions described in this disclosure.
So far, embodiments of the present disclosure have been described in detail. Some details that are well known in the art have not been described in order to avoid obscuring the concepts of the present disclosure. It will be fully apparent to those skilled in the art from the foregoing description how to practice the presently disclosed embodiments.
Although some specific embodiments of the present disclosure have been described in detail by way of example, it should be understood by those skilled in the art that the foregoing examples are for purposes of illustration only and are not intended to limit the scope of the present disclosure. It will be understood by those skilled in the art that various changes may be made in the above embodiments or equivalents may be substituted for elements thereof without departing from the scope and spirit of the present disclosure. The scope of the present disclosure is defined by the appended claims.
Claims (13)
1. An intelligent computing power scheduling method, comprising:
after receiving user service demand information, analyzing the user service demand into computing power demand, and dividing service grades according to the computing power demand;
acquiring cloud resources required by service function deployment from an enabling platform according to the service type and the divided service levels;
according to the service level, selecting matched deployment nodes from the acquired cloud resources, and deploying user virtual machines and virtual service gateways on the deployment nodes;
and configuring virtual service gateway routing so as to route user service traffic to a specified node to realize service online.
2. The method of claim 1, wherein after being online, further comprising:
monitoring the change situation of the service demand along with time to predict the change trend of the service demand;
and if the service level of the predicted service requirement changes, executing a step of acquiring cloud resources required by the service deployment function from the enabling platform according to the service type and the predicted service level.
3. The method of claim 2, wherein,
deploying the user virtual machine and the virtual service gateway on the deployment node comprises:
deploying a user virtual machine and a virtual service gateway on the deployment node by utilizing a Network Function Virtualization Orchestrator (NFVO);
predicting the change trend of the business demand comprises the following steps:
and predicting the change trend of the service demand by using a long-time memory model (LSTM).
4. The method of any one of claims 1-3,
the computing power requirement comprises at least one of a network requirement or a computing requirement, wherein the network requirement comprises time delay, bandwidth and packet loss rate, and the computing requirement comprises the number of CPU cores, memory, accelerated resource types and accelerated resource quantity.
5. The method of claim 4, wherein,
the cloud resources include core cloud computing power resources and edge cloud computing power resources.
6. An intelligent computing power scheduling apparatus, comprising:
the analysis module is configured to analyze the user service requirements into computing power requirements after receiving the user service requirement information, and perform service grade division according to the computing power requirements;
the cloud resource acquisition module is configured to acquire cloud resources required by service function deployment from the enabling platform according to the service types and the divided service levels;
the function deployment module is configured to select a matched deployment node from the acquired cloud resources according to the service level, and deploy a user virtual machine and a virtual service gateway on the deployment node;
and the network configuration module is configured to configure the virtual service gateway route so as to route the user service flow to the specified node to realize the on-line of the service.
7. The apparatus of claim 6, further comprising:
the monitoring module is configured to monitor the change situation of the service demand along with time after the service is online so as to predict the change trend of the service demand;
the cloud resource obtaining module is further configured to execute an operation of obtaining the cloud resources required for deploying the business function from the enabling platform according to the business type and the predicted service level if the service level of the predicted business requirement changes.
8. The apparatus of claim 7, wherein,
the function deployment module is configured to deploy a user virtual machine and a virtual service gateway on the deployment node by using a Network Function Virtualization Orchestrator (NFVO);
the monitoring module is configured to predict a trend of the change of the business demand by using the long-time memory model LSTM.
9. The apparatus of any one of claims 6-8,
the computing power requirement comprises at least one of a network requirement or a computing requirement, wherein the network requirement comprises time delay, bandwidth and packet loss rate, and the computing requirement comprises the number of CPU cores, memory, accelerated resource types and accelerated resource quantity.
10. The apparatus of claim 9, wherein,
the cloud resources include core cloud computing power resources and edge cloud computing power resources.
11. An intelligent computing power scheduling apparatus, comprising:
a memory configured to store instructions;
a processor coupled to the memory, the processor configured to perform implementing the method of any of claims 1-5 based on instructions stored by the memory.
12. An intelligent computing power scheduling system comprising an intelligent computing power scheduling apparatus according to any one of claims 6 to 11, and
and the enabling platform is configured to feed back the cloud resources required by the service deployment function to the intelligent computing power scheduling device according to the service type and the service level provided by the intelligent computing power scheduling device.
13. A computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions which, when executed by a processor, implement the method of any one of claims 1-5.
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Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113852950A (en) * | 2021-06-28 | 2021-12-28 | 山东浪潮科学研究院有限公司 | Computing network-oriented intelligent mobility tracking scheduling method and device |
| CN113934635A (en) * | 2021-10-21 | 2022-01-14 | 江苏安超云软件有限公司 | Method for providing cloud service with equal computing power based on heterogeneous processor and application |
| CN114327915A (en) * | 2022-03-08 | 2022-04-12 | 北京蚂蚁云金融信息服务有限公司 | Method and device for online resource allocation |
| CN114866430A (en) * | 2022-03-29 | 2022-08-05 | 北京智芯微电子科技有限公司 | Calculation force prediction method for edge calculation, calculation force arrangement method and system |
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| CN115802321A (en) * | 2022-11-04 | 2023-03-14 | 中国联合网络通信集团有限公司 | Method, system, electronic device and storage medium for arranging service functions |
| CN115964184A (en) * | 2023-03-16 | 2023-04-14 | 北京大学 | Method, apparatus and storage medium for determining computing power resources |
| CN116909756A (en) * | 2023-09-13 | 2023-10-20 | 中移(苏州)软件技术有限公司 | Cross-cloud service method and device, electronic equipment and storage medium |
| WO2024007880A1 (en) * | 2022-07-06 | 2024-01-11 | 大唐移动通信设备有限公司 | Network function determination method and device, and storage medium |
| WO2024125114A1 (en) * | 2022-12-13 | 2024-06-20 | 华为云计算技术有限公司 | Resource allocation method and apparatus based on cloud service |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2012083771A1 (en) * | 2010-12-24 | 2012-06-28 | 中兴通讯股份有限公司 | Cloud computing system and method |
| CN103164286A (en) * | 2013-03-12 | 2013-06-19 | 无锡云动科技发展有限公司 | Implement method, resource manager and cloud calculating system of cloud computing platform arrangement |
| CN104954496A (en) * | 2015-06-30 | 2015-09-30 | 北京奇虎科技有限公司 | Cloud resource allocation method and device |
| CN106055380A (en) * | 2016-05-20 | 2016-10-26 | 郑州丞极信息科技有限责任公司 | Method and system for integration of service servers |
| CN107404523A (en) * | 2017-07-21 | 2017-11-28 | 中国石油大学(华东) | Cloud platform adaptive resource dispatches system and method |
| CN108933795A (en) * | 2017-05-22 | 2018-12-04 | 中兴通讯股份有限公司 | The processing method and processing device of cloud call center |
| CN110417686A (en) * | 2019-06-12 | 2019-11-05 | 北京因特睿软件有限公司 | Cloud resource dynamic scheduling system |
-
2019
- 2019-12-09 CN CN201911246796.8A patent/CN113037786B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2012083771A1 (en) * | 2010-12-24 | 2012-06-28 | 中兴通讯股份有限公司 | Cloud computing system and method |
| CN103164286A (en) * | 2013-03-12 | 2013-06-19 | 无锡云动科技发展有限公司 | Implement method, resource manager and cloud calculating system of cloud computing platform arrangement |
| CN104954496A (en) * | 2015-06-30 | 2015-09-30 | 北京奇虎科技有限公司 | Cloud resource allocation method and device |
| CN106055380A (en) * | 2016-05-20 | 2016-10-26 | 郑州丞极信息科技有限责任公司 | Method and system for integration of service servers |
| CN108933795A (en) * | 2017-05-22 | 2018-12-04 | 中兴通讯股份有限公司 | The processing method and processing device of cloud call center |
| CN107404523A (en) * | 2017-07-21 | 2017-11-28 | 中国石油大学(华东) | Cloud platform adaptive resource dispatches system and method |
| CN110417686A (en) * | 2019-06-12 | 2019-11-05 | 北京因特睿软件有限公司 | Cloud resource dynamic scheduling system |
Cited By (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113852950A (en) * | 2021-06-28 | 2021-12-28 | 山东浪潮科学研究院有限公司 | Computing network-oriented intelligent mobility tracking scheduling method and device |
| CN113852950B (en) * | 2021-06-28 | 2023-06-09 | 山东浪潮科学研究院有限公司 | Intelligent mobility tracking scheduling method and device for computing network |
| CN113934635A (en) * | 2021-10-21 | 2022-01-14 | 江苏安超云软件有限公司 | Method for providing cloud service with equal computing power based on heterogeneous processor and application |
| CN113934635B (en) * | 2021-10-21 | 2022-07-19 | 江苏安超云软件有限公司 | Method for providing cloud service with equal computing power based on heterogeneous processor and application |
| CN114327915A (en) * | 2022-03-08 | 2022-04-12 | 北京蚂蚁云金融信息服务有限公司 | Method and device for online resource allocation |
| CN114327915B (en) * | 2022-03-08 | 2022-06-28 | 北京蚂蚁云金融信息服务有限公司 | Method and device for online resource allocation |
| CN114866430A (en) * | 2022-03-29 | 2022-08-05 | 北京智芯微电子科技有限公司 | Calculation force prediction method for edge calculation, calculation force arrangement method and system |
| CN115086720A (en) * | 2022-06-14 | 2022-09-20 | 烽火通信科技股份有限公司 | Network path calculation method and device for live broadcast service |
| CN115086720B (en) * | 2022-06-14 | 2023-06-09 | 烽火通信科技股份有限公司 | Network path calculation method and device for live broadcast service |
| WO2024007880A1 (en) * | 2022-07-06 | 2024-01-11 | 大唐移动通信设备有限公司 | Network function determination method and device, and storage medium |
| CN115311869B (en) * | 2022-07-29 | 2023-07-18 | 重庆长安汽车股份有限公司 | Road event acquisition method and system based on intelligent network-connected automobile calculation force sharing and automobile |
| CN115311869A (en) * | 2022-07-29 | 2022-11-08 | 重庆长安汽车股份有限公司 | Road event obtaining method and system based on intelligent network connection automobile computing power sharing and automobile |
| CN115802321A (en) * | 2022-11-04 | 2023-03-14 | 中国联合网络通信集团有限公司 | Method, system, electronic device and storage medium for arranging service functions |
| CN115802321B (en) * | 2022-11-04 | 2024-04-05 | 中国联合网络通信集团有限公司 | Service function arrangement method, system, electronic equipment and storage medium |
| WO2024125114A1 (en) * | 2022-12-13 | 2024-06-20 | 华为云计算技术有限公司 | Resource allocation method and apparatus based on cloud service |
| CN115964184B (en) * | 2023-03-16 | 2023-06-13 | 北京大学 | Method, device and storage medium for determining computing power resource |
| CN115964184A (en) * | 2023-03-16 | 2023-04-14 | 北京大学 | Method, apparatus and storage medium for determining computing power resources |
| CN116909756A (en) * | 2023-09-13 | 2023-10-20 | 中移(苏州)软件技术有限公司 | Cross-cloud service method and device, electronic equipment and storage medium |
| CN116909756B (en) * | 2023-09-13 | 2024-01-26 | 中移(苏州)软件技术有限公司 | Cross-cloud service method and device, electronic equipment and storage medium |
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